text: // set the dimensions and margins of the graph
  const margin = { top: 30, right: 30, bottom: 30, left: 50 },
    width = 460 - margin.left - margin.right,
    height = 400 - margin.top - margin.bottom;
  
  // append the svg object to the body of the page
  const svg = d3.select("#uniform")
    .append("svg")
    .attr("width", width + margin.left + margin.right)
    .attr("height", height + margin.top + margin.bottom)
    .append("p")
    .attr("transform", `translate(${margin.left},${margin.top})`);
  
  // get the data
  d3.csv("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/1_OneNum.csv").then(function (data) {
  
    // add the x Axis
    const x = d3.scaleLinear()
      .domain([0, 1000])
      .range([0, width]);
    svg.append("g")
      .attr("transform", `translate(0, ${height})`)
      .call(d3.axisBottom(x));
  
    // add the y Axis
    const y = d3.scaleLinear()
      .range([height, 0])
      .domain([0, 0.01]);
    svg.append("g")
      .call(d3.axisLeft(y));
  
    // Compute kernel density estimation
    const kde = kernelDensityEstimator(kernelEpanechnikov(7), x.ticks(40))
    const density = kde(data.map(function (d) { return d.price; }))
  
    // Plot the area
    svg.append("path")
      .attr("class", "mypath")
      .datum(density)
      .attr("fill", "#69b3a2")
      .attr("opacity", ".8")
      .attr("stroke", "#000")
      .attr("stroke-width", 1)
      .attr("stroke-linejoin", "round")
      .attr("d", d3.line()
        .curve(d3.curveBasis)
        .x(function (d) { return x(d[0]); })
        .y(function (d) { return y(d[1]); })
      );
  
  });
  
  
  // Function to compute density
  function kernelDensityEstimator(kernel, X) {
    return function (V) {
      return X.map(function (x) {
        return [x, d3.mean(V, function (v) { return kernel(x - v); })];
      });
    };
  }
  function kernelEpanechnikov(k) {
    return function (v) {
      return Math.abs(v /= k) <= 1 ? 0.75 * (1 - v * v) / k : 0;
    };
  }
  